In this paper, we introduce a learnable aggregation method that is able to capture the short-time spatio-temporal structure between deep visual features simultaneously. This is particularly important for real-fake expression prediction, since learning micro-emotional interdependencies from face parts as well as their sequential structure can be essential to distinguish the real-fake expressions as we mentioned. To advance the performance, we use high-level convolution features (conv5) of CNN that are finetuned for emotion detection [1]}.
